Research-backed education · NISM-Series-XII
Nifty Options, XGBoost & AI — Free Research for Indian Retail Traders
300+ free articles on options trading, machine learning models, crypto, and Hindi finance education. No paid course, no black box — just honest, build-your-own-AI thinking.
- Step-by-step XGBoost and LightGBM guides for Nifty and Bank Nifty option chains
- Complete Greek, IV rank, PCR, and position sizing explainers with worked examples
- SEBI 2026 algo trading rules, Indian crypto taxation, and broker comparisons
- Hindi guides for beginners, plus a free Telegram community for questions
- Honest, reproducible backtests with costs, drawdowns and walk-forward windows included, so every claim can be verified
Research categories
Choose your topic. Go deep.
Each category is built from real research, not rehashed summaries.
Options Trading
Nifty, Bank Nifty, Greeks, strategies, and SEBI rules explained with real data.
107 articlesCrypto & Bitcoin
Halving cycles, on-chain analytics, Indian tax rules, and trading psychology.
60 articlesML & AI for Trading
XGBoost, LightGBM, feature engineering, walk-forward validation, and backtesting.
101 articlesIndia & Regulation
SEBI rules, algo trading compliance, broker comparisons, and retail trader guides.
26 articlesHindi Guides
Options basics, crypto, SEBI rules — explained in Hindi for broader access.
6 articlesMost read
Start here
XGBoost vs LightGBM for Nifty Options — Honest Walk-Forward Benchmark (2026)
MLWalk-Forward Validation for Options Strategies: Anchored vs Rolling Windows
MLNifty Option Chain Analysis with AI — OI, PCR, Greeks That Actually Matter
SEBI's New Algo Trading Rules — July 2026: What Retail Traders Need to Know
RegulationBitcoin Halving Cycle 2026: Where BTC Could Bottom
CryptoXGBoost Feature Engineering for Nifty Options — 12 Features + Python
MLMethod
The 5-layer AI trading pipeline
01 — Data Engine
Collect, store, and validate market data with point-in-time correctness.
02 — Feature Engineering
Build leakage-free features: OI, PCR, Greeks, order flow, regime.
03 — Predictor
XGBoost or LightGBM classification with walk-forward validation.
04 — Risk Filter
DTE, Vega exposure, probability bands, regime detection.
05 — Executor
Position sizing by premium risk. Never risk more than you can afford to lose.
Why this pipeline matters for Indian traders: most retail participants lose to two things — randomness and lack of a repeatable process. The 5-layer approach forces every trade into a decision tree. Data is collected with point-in-time correctness so no future leak contaminates training. Features such as Open Interest, PCR, and VWAP are computed exactly as they existed at decision time. The predictor outputs a probability band, not a certainty, and the risk filter overlays DTE and Vega constraints so a statistically good signal is never turned into a ruinous position. Finally, the executor converts probability into position size using premium risk — the rupee amount you are willing to lose — never notional exposure. If you are new, begin with the walk-forward validation guide, then the feature engineering tutorial, and only then touch live markets.
How we avoid overfitting
Why honesty matters more than a pretty backtest
- Walk-forward, not train-test accuracy — every model is judged on Out-of-Sample windows it has never seen during training.
- Transaction realism — brokerage, STT, exchange charges, and slippage are deducted in every simulation.
- Regime awareness — a strategy that only works in bull phases is labelled as such, never sold as a universal edge.
- No cherry-picked dates — published results include losing streaks and maximum drawdown, not just the winning months.
- Reproducible code — the Python notebooks are shared openly so any reader can re-run and verify the numbers.
This is the contract every article on this site keeps. The options, crypto, and machine-learning research here is built to be verified, challenged, and improved — not worshipped. If a claim in an article surprises you, check the data, re-run the walks, and ask in the Telegram community; the conversation is how the research gets better.
Frequently asked
Before you start
Is this financial advice?
No. All content is educational only. Shakti Tiwari is NISM-Series-XII certified as an educator, not a SEBI-registered Research Analyst or Investment Adviser. Always verify before acting.
Do I need to pay for anything?
No. All 300+ articles, tutorials, and the Telegram channel are free. The books are optional and priced at ₹199-₹449 on Amazon. There is no paid course.
Can I run these models on my phone?
Yes. The research covers running XGBoost and quantized models on Android using Termux and Ollama. You don't need expensive hardware.
What markets does this cover?
Nifty 50, Bank Nifty, and crypto (Bitcoin, Ethereum). The XGBoost and feature engineering principles apply to any liquid options market.
Free forever
No paid course. No black box.
Every article, notebook, and explainer on this site is free. The only paid products are two Amazon Kindle e-books written by Shakti Tiwari, priced from ₹199, and buying them is entirely optional. Everything you need to build and validate your own AI trading workflow lives here — from reading raw option-chain data to training XGBoost models and running them on a phone. If you value this research and want a community of like-minded traders, join the free Telegram group where questions are answered every day.